Energy Efficient Hybrid Routing Protocol Based on the Artificial Fish Swarm Algorithm and Ant Colony Optimisation for...

Area of Science:

• Computer Science
• Network Engineering
• Artificial Intelligence

Background:

• Wireless Sensor Networks (WSNs) face significant challenges in energy consumption and network lifetime extension.
• Ant Colony Optimisation (ACO) inspired energy-efficient routing but suffers from slow convergence and stagnation.
• Existing ACO-based algorithms struggle with efficient route discovery in large-scale networks.

Purpose of the Study:

• To propose a hybrid routing algorithm combining Artificial Fish Swarm Algorithm (AFSA) and ACO for WSNs.
• To overcome the limitations of slow convergence and stagnation inherent in traditional ACO algorithms.
• To enhance energy efficiency and extend the operational lifetime of Wireless Sensor Networks.

Main Methods:

• Developed a hybrid routing algorithm integrating AFSA for rapid initial route discovery and ACO for refinement.
• Implemented a novel route discovery mechanism combining AFSA's crowd factor with ACO's pseudo-random selection strategy.
• Introduced an improved pheromone update mechanism that incorporates node energy levels and path length considerations.

Main Results:

• The proposed hybrid algorithm effectively avoids local optimization and stagnation issues common in routing protocols.
• Demonstrated accelerated routing convergence, particularly noticeable and beneficial in large-scale network environments.
• Simulation results indicate a significant improvement in the overall network lifetime of WSNs.

Conclusions:

• The hybrid AFSA-ACO routing algorithm offers a robust solution for enhancing WSN performance.
• The integration of AFSA and ACO provides a synergistic approach to overcoming routing challenges in WSNs.
• This research contributes to extending the longevity and efficiency of Wireless Sensor Networks through advanced routing strategies.